Archive for November, 2017

New research is showing how using advanced computer-aided analysis of medical images could help doctors to make great leaps in precision and personalised medicine.

Companies like Bitplane.com design and develop the software which will enable doctors to analyse medical images with ever-greater depth and granularity. Many specialists believe we’ll be able to spot problems in organs before the first symptoms are noticed. A recent study found that researchers could “predict” patient five-year mortality with nearly 70% accuracy. This is very promising indeed, as it could represent the chance to intervene and turn someone’s health around before it starts to present problems.

What is precision medicine?

Precision medicine, according to the US’ National Institutes of Health, defines precision medicine as a new approach to treating diseases that focuses on early or pre-emptive identification and intervention. This sort of medicine looks at a person’s genome, their environment and their lifestyle to formulate a unique risk and intervention programme.

Precision or personalised medicine relies on identifying the biomarkers that are known and accurate indicators of either a risk of a disease or the signs of its early development. Radiology and image analysis, as well as genetics, plays a vital role here.

X-rays and other medical imaging techniques have been used for decades to look for diseases, but they are only useful when the problems are relatively advanced. By using image analysis and machine-learning, doctors and patients will be meeting trouble – and hopefully combatting it – way before the halfway mark.

Medical imaging will also save a lot of time and money because it has the potential to do the same job, with the same (if not better) accuracy than microscopy, DNA analysis and biopsy, which are often used – and paid for – together.

How will it all work?

Medical researchers and developers will use thousands of CT images, X-rays of organs and tissue samples and so on to teach computers how to look for biomarkers that predict the development of disease. The eventual outcome of each patient (including mortalities) is already known, so by teaching the machines how to use the data alongside deep learning methods and genetic information, we’ll have computers that can spot the early signs of disease.

These machines will use data to identify patients most at risk of various diseases, as well as to predict their most likely prognoses in ways that are more sensitive and accurate than doctors can manage. Time, as ever, is of the essence and it’s hoped that by preventative medicine or early intervention, patient outcomes will be vastly improved.

If someone is, for example, at an increased risk of lung cancer because of their genotype, then a combination of a smoking cessation programme followed up by yearly chest scans could see that patient alive and well 15 years down the line.

Similarly, the early changes involved in chronic diseases like osteoarthritis could be spotted before the joints actually start aching, allowing the patient to adopt new exercise and dietary habits, as well as take whatever medical interventions are available to ward off the disease.